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#!/bin/sh
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# Train a GT4HistOCR Calamari model
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# (or rather 5 for voted prediction)
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set -e
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self=`realpath $0`
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self_dir=`dirname "$self"`
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cd $self_dir
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DATA_SUBDIR=data
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get_from_annex() {
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annex_get 'GT4HistOCR/corpus/*.tar.bz2'
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}
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get_from_web() {
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download_to 'https://zenodo.org/record/1344132/files/GT4HistOCR.tar?download=1' 'GT4HistOCR'
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}
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. $self_dir/qurator_data_lib.sh
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handle_data
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rm -rf /tmp/train-calamari-gt4histocr.*
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TMPDIR=`mktemp -d /tmp/train-calamari-gt4histocr.XXXXX`
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echo "Unpacking dataset tar files to $TMPDIR"
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for tar in $DATA_SUBDIR/GT4HistOCR/corpus/*.tar.bz2; do
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tar xf $tar -C $TMPDIR
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done
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echo "Removing dta19/1882-keller_sinngedicht/04970.nrm.png (Broken PNG)"
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rm -f $TMPDIR/dta19/1882-keller_sinngedicht/04970.*
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export PYTHONUNBUFFERED=1 # For python + tee
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outdir=$DATA_SUBDIR/calamari-models/GT4HistOCR
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mkdir -p $outdir
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calamari-cross-fold-train \
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--files \
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"$TMPDIR/*/*/*.png" \
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--best_models_dir $outdir \
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--early_stopping_frequency=0.25 \
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--early_stopping_nbest=5 \
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--batch_size=128 \
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--n_folds=5 \
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--max_parallel_models=1 \
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--display=0.01 \
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2>&1 | tee $outdir/train.`date -Iminutes`.log
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